A Reformulation of the Laminar Kinetic Energy Model to Enable Multi-mode Transition Predictions
Abstract The paper describes the development of a novel transition/turbulence model based on the laminar kinetic energy concept. The model is intended as a base framework for data-driven improvements. Starting from a previously developed framework, mainly aimed at separated-flow transition predictions, suitable terms for model generalization are identified and reformulated for handling different transition modes, namely bypass and separated-flow modes. The ideology for the definition of new terms has its roots in mixing phenomenological and correlation-based arguments, ensuring generality and flexibility and allowing a variety of lines of action for improving model components via machine-learning approaches. The model calibration, carried out with reference to flat plate test cases subjected to different pressure gradients and freestream turbulence levels, is discussed in detail. Although the constructed model is calibrated on a group of classic flat plat cases, the validation campaign, mostly carried out on gas turbine cascades, demonstrates its ability to predict transitional flows with engineering accuracy. Finally, while the model is not specifically developed for natural transition predictions, satisfactory predictions are obtained in scenarios with low freestream turbulence for flat plate and airfoil flows.
Authors
- Yuan Fang (ORCID: https://orcid.org/0000-0001-5196-8706)
- Richard D. Sandberg (ORCID: https://orcid.org/0000-0001-5199-3944)
- Roberto Pacciani (ORCID: https://orcid.org/0000-0002-7242-0480)
- Michele Marconcini (ORCID: https://orcid.org/0000-0002-4391-0093)
- Leonardo Metti
Institutions
- The University of Melbourne (AU)
- University of Florence (IT)
Publication Details
- Journal
- Flow Turbulence and Combustion
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1007/s10494-024-00590-y
- Citations
- 3
- Primary Topic
- Computational Fluid Dynamics and Aerodynamics
- Type
- article
- Field-Weighted Citation Impact
- 1.12
Funders
- Università degli Studi di Firenze